TY - JOUR
KW - Physical and Theoretical Chemistry
KW - Materials Chemistry
KW - Biochemistry
KW - Computational Mathematics
KW - Computer Science Applications
AU - Peña‐Guerrero J
AU - Nguewa PA
AU - García‐Sosa AT
AB - Machine learning (ML) is becoming capable of transforming biomolecular interaction description and calculation, promising an impact on molecular and drug design, chemical biology, toxicology, among others. The first improvements can be seen from biomolecule structure prediction to chemical synthesis, molecular generation, mechanism of action elucidation, inverse design, polypharmacology, organ or issue targeting of compounds, property and multiobjective optimization. Chemical design proposals from an algorithm may be inventive and feasible. Challenges remain, with the availability, diversity, and quality of data being critical for developing useful ML models; marginal improvement seen in some cases, as well as in the interpretability, validation, and reuse of models. The ultimate aim of ML should be to facilitate options for the scientist to propose and undertake ideas and for these to proceed faster. Applications are ripe for transformative results in understudied, neglected, and rare diseases, where new data and therapies are strongly required. Progress and outlook on these themes are provided in this study.
BT - WIREs Computational Molecular Science
DO - 10.1002/wcms.1513
LA - eng
N2 - Machine learning (ML) is becoming capable of transforming biomolecular interaction description and calculation, promising an impact on molecular and drug design, chemical biology, toxicology, among others. The first improvements can be seen from biomolecule structure prediction to chemical synthesis, molecular generation, mechanism of action elucidation, inverse design, polypharmacology, organ or issue targeting of compounds, property and multiobjective optimization. Chemical design proposals from an algorithm may be inventive and feasible. Challenges remain, with the availability, diversity, and quality of data being critical for developing useful ML models; marginal improvement seen in some cases, as well as in the interpretability, validation, and reuse of models. The ultimate aim of ML should be to facilitate options for the scientist to propose and undertake ideas and for these to proceed faster. Applications are ripe for transformative results in understudied, neglected, and rare diseases, where new data and therapies are strongly required. Progress and outlook on these themes are provided in this study.
PB - Wiley
PY - 2021
T2 - WIREs Computational Molecular Science
TI - Machine learning, artificial intelligence, and data science breaking into drug design and neglected diseases
SN - 1759-0876, 1759-0884
ER -